This application addresses broad Challenge Area (03): Biomarker Discovery and Validation and specific Challenge Topic, 03-HL-101: Identify and validate clinically relevant, quantifiable biomarkers of diagnostic and therapeutic responses for blood, vascular, cardiac, and respiratory tract dysfunction. Sleep loss is common in the American population. Some groups, such as health care workers (physicians, nurses), are particularly at risk for sleep loss because of their work schedules. Sleep loss has a major impact on cognitive performance with an increased risk of vehicular crashes, and medical errors. It also results in abnormalities in glucose handling (insulin resistance), increased obesity rates, and is a risk factor for cardiovascular disease. There are, however, major differences in the response to sleep loss between individuals and recent data indicate that this difference is in large part genetic. While these consequences and individual differences are known, there is currently no validated biomarker to assess sleep loss. This proposal plans to utilize a novel proteomic approach to identify biomarkers for sleep loss. Studies will be done in healthy volunteers, who had blood samples taken every 4 hours during a normal day, as well as during progressive sleep deprivation, and during recovery sleep. These human studies have already been conducted in 10 subjects who are relatively resistant to the effects of sleep deprivation (low responders) and 10 who show a marked behavioral response (high responders). PUBLIC HEALTH RELEVANCE: A state of the art proteomic approach will be applied to these already collected plasma samples to address two fundamental questions: (1) are there changes in proteins in blood with sleep deprivation, controlling for circadian influences, that can act as biomarkers for sleep loss;and (2) is the magnitude of change with sleep loss in candidate biomarkers different between subjects with a low response to sleep loss to those with a high response? To address these questions we will use an in-depth quantitative proteomic approach, combining isotopic labeling, a multidimensional protein identification technology (MudPIT) protocol and mass spectrometry, for high confidence identification of protein changes in the plasma of sleep deprived subjects. The proposed studies are designed to identify a biomarker for sleep loss. We hypothesize that there are molecular signatures that reflect the state of sleepiness. We will use a novel proteomic approach to identify proteins that change in response to sleep loss.